Introduction  Articulated 3D objects—models consisting of interconnected movable parts—are fundamental to many cutting-edge technologies including robotics, augmented reality (AR), virtual reality (VR), and animation. The ability to realistically generate and manipulate such objects enables more immersive simulations, flexible animations, and precise robot interaction with complex mechanical devices. Traditional methods for modeling articulated objects often depend on intensive optimization routines requiring dense multi-view image data or necessitate training expensive feed-forward networks on large datasets. These approaches either demand substantial manual labeling or produce coarse approximations that fail to capture intricate surface details.  Recent breakthroughs in 3D generative modeling, particularly with 3D diffusion models like Trellis, have revolutionized static object…

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